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Kruppa, J., Schwarz, A., Arminger, G., & Ziegler, A. (2013). Consumer Credit Risk: Individual Probability Estimates Using Machine Learning. Expert Systems with Applications, 40, 5125-5131.
https://doi.org/10.1016/j.eswa.2013.03.019

has been cited by the following article:

• TITLE: Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

  AUTHORS: Chunlan Wang, Mahmut Rustem Sen, Bin Yao, Michal Certik, Koloina A. Randrianarivony

  KEYWORDS: Machine Learning, Credit Rating, Credit Portfolio Management, Random Forest Methodology, Machine Learning Training and Model Validation, Artificial Intelligence

  JOURNAL NAME: Journal of Financial Risk Management, Vol.10 No.3, September 18, 2021

  ABSTRACT: Credit risk ratings consist of assessing the creditworthiness of the issuer and gauge the risks associated with buying its debt. Any delay in updating the credit risk ratings could have a severe impact on the financial system such as the financial crisis in 2008. This paper discusses a case that leverages emerging technology and breakthrough cognitive analytics in the financial industry. It specifically describes the design and implementation of a predictive modeling case based on the Machine Learning Approach and its application in credit risk forecasting and portfolio management. Using big data and Machine Learning, it is possible to improve credit risk analysis and forecasting by allowing the algorithms to search for patterns using large sets of data.